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Author(s): Mohammed A. Mamun (corresponding author) [1,2,3]; Firoj Al-Mamun [1,2,3]; Md Emran Hasan [3,4]; Nitai Roy [5]; Moneerah Mohammad ALmerab [6]; Mohammad Muhit [2]; Mst. Sabrina Moonajilin [1] 1. Introduction [...], Background: Diabetes management poses significant challenges worldwide, with individuals often facing increased risks of mental health issues, including suicidal behaviors. While traditional statistical methods have been used to assess risk factors, this study introduces machine learning techniques to predict suicidal behaviors among diabetic patients. Therefore, this study aims to investigate the prevalence and risk factors of suicidal behaviors among individuals with diabetes in Bangladesh using both traditional statistical methods and machine learning models. Methods: A cross-sectional study was carried out with 390 diabetic patients, assessing demographic characteristics, diabetes-related variables, mental health indicators, and suicidal behaviors. Statistical analyses including chi-square tests and logistic regression, and various machine learning models, were employed to examine associations and predict risk factors. Results: This study found that 13.8% of the individuals with diabetes reported lifetime suicidal behavior and 9.0% reported past-year suicidal behavior. Significant risk factors associated with higher rates of suicidal behaviors included younger age, marital status, lower monthly income, higher educational attainment, being insulin users, not having a family history of diabetes, and suffering from anxiety. Among the machine learning models tested, the CatBoost model outperformed others, obtaining low log loss values of 0.45 and 0.32 for lifetime and past-year suicidal behaviors, respectively. CatBoost demonstrated excellent accuracy rates of 83.97% and 88.46%, significantly higher than those of traditional logistic regression models. The most significant predictors identified by machine learning were treatment type, followed by occupation, anxiety, age group, and having chronic diseases. Conclusion: The findings suggest the urgent need for comprehensive interventions and support systems for individuals managing diabetes, particularly those at higher risk of suicidal behaviors. Machine learning offers improved predictive power and the ability to identify complex patterns in risk factors. Collaborative efforts between healthcare providers, mental health professionals, and public health initiatives are essential for prioritizing mental well-being in diabetes management.

The Internet Disorder Scale-Short Form (IDS9-SF) is a validated instrument assessing internet disorder which modified the internet gaming disorder criteria proposed in the fifth edition of the Diagnostic and Statistical Manual of Mental Disorders (DSM-5). However, the relationships between the nine items in the IDS9-SF are rarely investigated. The present study used network analysis to investigate the features of the IDS9-SF among three populations in Bangladesh, Iran, and Pakistan. Data were collected (N = 1901; 957 [50.3%] females; 666 [35.0%] Pakistani, 533 [28.1%] Bangladesh, and 702 [36.9%] Iranians) using an online survey platform (e.g., Google Forms). All the participants completed the IDS9-SF. The central-stability-coefficients of the nine IDS9-SF items were 0.71, 0.89, 0.96, 0.98, 0.98, 1.00, 0.67, 0.79, and 0.91, respectively. The node centrality was stable and interpretable in the network. The Network Comparison Test (NCT) showed that the network structure had no significant differences among Pakistani, Bangladeshi, and Iranian participants (p-values = 0.172 to 0.371). Researchers may also use the IDS9-SF to estimate underlying internet addiction for their target participants and further explore and investigate the phenomenon related to internet addiction., Author(s): Li Li [sup.1] , Mohammed A. Mamun [sup.2] [sup.14] [sup.15] [sup.16] , Firoj Al-Mamun [sup.2] [sup.14] [sup.16] , Irfan Ullah [sup.3] , Ismail Hosen [sup.2] , Syed Ahsan Zia [...]

Aim: This study aimed to evaluate the food safety knowledge, attitudes, and practices (KAP) of Bangladeshi consumers and investigate their associations with various demographic factors. Methods: This national cross‐sectional study utilized a face‐to‐face survey using a structured questionnaire to recruit 1400 adult consumers between September 2022 and November 2022. Multiple linear regression analysis was employed to determine the factors associated with the food safety KAP. Results: The current findings revealed that consumers had moderate knowledge (overall correct response rate of 65.3% with a mean score of 13.06 ± 2.88 on a scale of 0 to 20), excellent attitudes (overall correct response rate for food safety knowledge was 81.8% with a mean score of 14.72 ± 2.72 on a scale of 0 to 18), and relatively poor practices (overall correct response rate was 51.7% with a mean score of 35.17 ± 11.2 on a scale of 0 to 68). Participants who were male, had higher education, were employed, were familiar with the Bangladesh Food Safety Authority, and residing outside the Mymensingh division, demonstrated greater food safety knowledge. However, participants from Barisal, Rangpur, and Dhaka, who were less educated, had fewer children, and only occasionally cooked, had negative attitudes about food safety. Additionally, individuals from all administrative divisions except Mymensingh and Chittagong, who were employed, had fewer children, had experienced food poisoning, and had inadequate food safety practices. Conclusion: These findings suggest that the Bangladeshi government should intensify efforts to enhance consumer awareness regarding food safety. [ABSTRACT FROM AUTHOR]

Background: Excessive daytime sleepiness (EDS) among adolescents poses significant risks to academic performance, mental health, and overall well-being. This study examines the prevalence and risk factors of EDS in adolescents in Bangladesh and utilizes machine learning approaches to predict the risk of EDS. Methods: A cross-sectional study was conducted among 1496 adolescents using a structured questionnaire. Data were collected through a two-stage stratified cluster sampling method. Chi-square tests and logistic regression analyses were performed using SPSS. Machine learning models, including Categorical Boosting (CatBoost), Extreme Gradient Boosting (XGBoost), Support Vector Machine (SVM), Random Forest (RF), K-Nearest Neighbors (KNN), and Gradient Boosting Machine (GBM), were employed to identify and predict EDS risk factors using Python and Google Colab. Results: The prevalence of EDS in the cohort was 11.6%. SHAP values from the CatBoost model identified self-rated health status, gender, and depression as the most significant predictors of EDS. Among the models, GBM achieved the highest accuracy (90.15%) and precision (88.81%), while CatBoost had comparable accuracy (89.48%) and the lowest log loss (0.25). ROC-AUC analysis showed that CatBoost and GBM performed robustly in distinguishing between EDS and non-EDS cases, with AUC scores of 0.86. Both models demonstrated the superior predictive performance for EDS compared to others. Conclusions: The study emphasizes the role of health and demographic factors in predicting EDS among adolescents in Bangladesh. Machine learning techniques offer valuable insights into the relative contribution of these factors, and can guide targeted interventions. Future research should include longitudinal and interventional studies in diverse settings to improve generalizability and develop effective strategies for managing EDS among adolescents. [ABSTRACT FROM AUTHOR]

Background: Numerous studies have examined substance use and smoking behavior among adolescents and university students. However, little is known about these behaviors among students undergoing university entrance tests, a critical transition period from adolescence to adulthood. The entrance test can significantly affect students' mental health, potentially leading to substance use. This study aims to investigate the prevalence of cigarette smoking and substance use among students taking these exams and the associated factors. Methods: A cross-sectional survey was carried out on September 4th and 11th, 2022 to collect data from 1,480 university entrance test-taking students using a convenience sampling technique. Chi-square tests and logistic regression were conducted using SPSS software. Besides, GIS mapping was used to visualize the distribution of substance use and smoking behavior across districts via ArcGIS. Results: The study found a 10% prevalence of current tobacco smoking and 4% substance use. Females (OR = 1.98; 95% CI: 1.38–2.85), urban residence (OR = 2.03; 95% CI: 1.42–2.88), repeater (OR = 1.45; 95% CI: 1.02–2.06), anxiety (OR = 1.55, 95% CI: 1.10–2.19), burnout (OR = 1.51, 95% CI: 1.00–2.12), and suicidal behavior (OR = 1.57; 95% CI: 1.03–2.40) were the significant factors for cigarette use. Whereas the urban residence (OR = 1.91; 95% CI: 1.11–3.31), anxiety (OR = 2.47, 95% CI: 1.45–4.20), and suicidal behavior (OR = 2.76; 95% CI: 1.55–4.92) significantly increased the risk of substance use. GIS analysis revealed males varied in substance use and females in tobacco smoking by district. Repeat test-takers were associated with district variations in both smoking and substance use. Conclusions: Educational institutions, public health authorities, and policymakers must implement mental health support and substance use prevention programs for students. Integrating mental health education, providing resources, and enforcing regulations can promote healthier coping strategies and reduce substance use risks among students. [ABSTRACT FROM AUTHOR]

Aim: This study aims to investigate the relationship between nurses' self‐efficacy and self‐esteem, and their job satisfaction. Design: A cross‐sectional study was conducted. Methods: Employing a random sampling method that included 234 nurses from three hospitals in Iran enrolled. This study utilized the General Self‐Efficacy Questionnaire, Coppersmith Self‐Esteem Inventory, and Minnesota Job Satisfaction Questionnaire. Descriptive analysis, independent t‐tests, Pearson correlation analyses, and linear regression were employed for data analysis. Results: The mean self‐efficacy score for nurses was 26.73 ± 5.62 (out of 40), while self‐esteem and job satisfaction scored 37.13 ± 6.87 (out of 50) and 68.27 ± 12.65 (out of 100), respectively. Significant correlations were found between self‐efficacy, self‐esteem, and job satisfaction. Moreover, self‐esteem and the age group >40 years were identified as important predictors of nurses' job satisfaction. This study highlights the influential role of self‐esteem in determining nurses' job satisfaction. [ABSTRACT FROM AUTHOR]

Prospective university students experience substantial academic stressors and psychological vulnerabilities, yet their mental health literacy (MHL) remains inadequately explored. This study investigates four dimensions of MHL - help-seeking behaviors, stigma, knowledge about mental health and understanding of mental illnesses. Besides, Geographic Information System (GIS) techniques are employed to analyze spatial disparities in MHL, which is the first in the context of MHL research. A total of 1,485 students were assessed for sociodemographic characteristics, admission-related variables, health behaviors and family histories of mental health issues. Data were analyzed using SPSS and ArcGIS software. Multivariable linear regression analyses unveiled predictors of the MHL dimensions, with gender, family income, admission test performance, smoking, alcohol and drug use, physical and mental health history, current depression or anxiety and family history of mental health and suicide incidents emerging as common predictors. GIS analysis unraveled notable regional disparities in MHL, particularly in knowledge of mental health and mental illness, with northern and some southern districts displaying higher literacy levels. In conclusion, these findings accentuate significant gender and sociodemographic inequalities in MHL among prospective university students, highlighting the imperative for targeted interventions to enhance MHL and foster mental well-being in this cohort. [ABSTRACT FROM AUTHOR]

Background: Students are one of the most vulnerable groups to suicide. Before the COVID-19 pandemic, a Bangladeshi study was conducted assessing their suicide patterns regarding gender-based associations. But how has the pandemic changed the Bangladeshi students' suicide patterns is not studied yet, which is investigated herein. Besides, for the first time, this study provides GIS-based distribution of suicide cases across the country's administrative district. Methods: As Bangladesh has no suicide surveillance system, this study utilized media reporting suicide cases following the prior studies. A total of 127 students' suicide cases from March 2020 to March 2021 were finally analyzed after eliminating the duplicate ones, and data were synthesized following the prior studies. Arc-GIS was also used to distribute the suicide cases across the administrative district. Results: Results revealed that female (72.4%; n = 92/127) was more prone to die by suicide than males. About 42.5% of the cases were aged between 14 and 18 years (mean age 16.44 ± 3.512 years). The most common method of suicide was hanging (79.5%; n = 101), whereas relationship complexities (15.7%), being emotional (12.6%), not getting the desired one (11%), conflict with a family member (9.4%), academic failure (9.4%), mental health problem (8.7%), sexual complexities (6.3%), scolded or forbidden by parents (3.9%) were the prominent suicide causalities. In respect to gender and suicide patterns, only the suicide stressor was significantly distributed, whereas the method of suicide was significantly associated with GIS-based distribution. However, a higher number of suicide cases was documented in the capital (i.e. Dhaka) and the northern region than in its surrounding districts. Conclusions: The findings reported herein are assumed to be helpful to identify the gender-based suicide patterns and suicide-prone regions in the time of the COVID-19 pandemic to initiate suicide prevention programs of the risky students. [ABSTRACT FROM AUTHOR]

Background: Palliative care services involve the psychological care of the caregivers of cancer patients. Psychological conditions, especially depression among caregivers, distort caregiving roles; thus, it can increase a patient's psychological suffering. Objective: To determine the prevalence of depression and associated coping strategies among caregivers of cancer patients at a rural cancer care facility. Methods: This cross‐sectional study was among 366 caregivers of cancer patients. The data was collected using a pretested questionnaire, where the symptoms of depression were assessed using the Patient Health Questionnaire‐9 at a cutoff of 10 out of 27. The coping strategies were assessed based on the Brief‐coping orientation to problems experienced Inventory. Logistic regression was used to determine the factors associated with depression. Results: The mean age of the participants was 39.01 (±11.50) years; most were females (60.38%). The prevalence of depression was 8.2%. The identified factors associated with increased likelihood of depression were coping strategies: active coping (aOR = 1.55, 95% Confidence Interval (CI) = 1.05–2.28, p = 0.026), denial (aOR = 1.62, 95% CI = 1.20–2.19, p = 0.001), and humor (aOR = 1.43, 95% CI = 1.11–1.84, p = 0.005). However, coping with positive reframing reduced the likelihood of depression (aOR = 0.70, 95% CI = 0.52–0.94, p = 0.019). There was no significant association between depression and social support. Conclusion: The lower prevalence of depression reported in this study than in the prior Ugandan studies reflects that depression severity among caregivers in rural settings is less prevalent because of the fewer care‐associated burdens they experience. Therefore, establishing palliative care near the patients can be a protective factor for caregivers' depression. In addition, the role of social support and coping strategies in depression might be helpful in mental health strategies. [ABSTRACT FROM AUTHOR]